CHARACTERIZING FATTY LIVER IN VIVO IN RABBITS, USING QUANTITATIVE ULTRASOUND
CHARACTERIZING FATTY LIVER IN VIVO IN RABBITS, USING QUANTITATIVE ULTRASOUND
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DOI：
10.1016/j.ultrasmedbio.2019.03.021
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发表时间：
2019-08-01
影响因子：
2.9
通讯作者：
Oelze, Michael L.
中科院分区：
文献类型：
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作者：
Nguyen, Trong N.;Podkowa, Anthony S.;Oelze, Michael L.
非酒精性脂肪性肝病（NAFLD）是慢性肝病最常见的原因，通常可导致纤维化、肝硬化、癌症和完全肝衰竭。肝活检是目前量化肝脂肪变性的标准治疗方法，但它会增加患者的风险，并且只对一小部分肝脏进行采样。评估NAFLD的成像方法包括通过磁共振成像（MRI）和剪切波弹性成像估计的质子密度脂肪分数。然而，MRI价格昂贵，剪切波弹性成像未被证明对肝脏的脂肪含量敏感（克雷默等人，2016）。另一方面，已观察到超声衰减和反向散射系数（BSC）对肝脏中的脂肪水平敏感（Lin等人，2015; Paige等人，2017）。在这项研究中，我们评估了在兔脂肪肝模型中使用衰减和BSC来量化体内肝脂肪变性。将兔维持在高脂肪饮食0、1、2、3或6周，每个饮食组3只兔（总N= 15）。使用中心频率为4.5 MHz的阵列换能器（L9-4）连接到Sonix-One扫描仪，以收集家兔体内的射频（RF）背散射数据。RF信号用于估计每只家兔的平均衰减和BSC。使用两种方法来参数化BSC（即，使用球形高斯模型的有效散射体直径和有效声学集中度以及使用主成分分析[PCA]的无模型方法）。来自BSC的PCA的2个主要分量捕获了变换数据的96%的方差，用于生成用于分类的支持向量机的输入特征。将家兔分为两个肝脏脂肪水平类别，使得大约一半的家兔处于低脂质类别（9%脂质肝脏水平）。衰减系数的斜率和中带拟合在低和高血脂脂肪类别之间提供了统计学显著差异（p值= 0.00014和p值= 0.007，使用双样本t检验）。所提出的BSC和衰减系数参数的无模型和基于模型的参数化对于区分低脂质与高脂质类别分别产生84.11%、82.93%和78.91%的分类准确度。结果表明，衰减和BSC分析可以区分兔脂肪肝模型中的低脂肪肝和高脂肪肝。(C)2019年世界医学和生物学超声联合会。All rights reserved.
Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and can often lead to fibrosis, cirrhosis, cancer and complete liver failure. Liver biopsy is the current standard of care to quantify hepatic steatosis, but it comes with increased patient risk and only samples a small portion of the liver. Imaging approaches to assess NAFLD include proton density fat fraction estimated via magnetic resonance imaging (MRI) and shear wave elastography. However, MRI is expensive and shear wave elastography is not proven to be sensitive to fat content of the liver (Kramer et al. 2016). On the other hand, ultrasonic attenuation and the backscatter coefficient (BSC) have been observed to be sensitive to levels of fat in the liver (Lin et al. 2015; Paige et al. 2017). In this study, we assessed the use of attenuation and the BSC to quantify hepatic steatosis in vivo in a rabbit model of fatty liver. Rabbits were maintained on a high-fat diet for 0, 1, 2, 3 or 6 wk, with 3 rabbits per diet group (total N= 15). An array transducer (L9-4) with a center frequency of 4.5 MHz connected to a Sonix-One scanner was used to gather radio frequency (RF) backscattered data in vivo from rabbits. The RF signals were used to estimate an average attenuation and BSC for each rabbit. Two approaches were used to parameterize the BSC (i.e., the effective scatterer diameter and effective acoustic concentration using a spherical Gaussian model and a model-free approach using a principal component analysis [PCA]). The 2 major components of the PCA from the BSCs, which captured 96% of the variance of the transformed data, were used to generate input features to a support vector machine for classification. Rabbits were separated into two liver fat-level classes, such that approximately half of the rabbits were in the low-lipid class (9% lipid liver level). The slope and the midband fit of the attenuation coefficient provided statistically significant differences (p value = 0.00014 and p value = 0.007, using a two-sample t test) between low and high-lipid fat classes. The proposed model-free and model-based parameterization of the BSC and attenuation coefficient parameters yielded classification accuracies of 84.11 %, 82.93 % and 78.91 % for differentiating low-lipid versus high-lipid classes, respectively. The results suggest that attenuation and BSC analysis can differentiate low-fat versus high-fat livers in a rabbit model of fatty liver disease. (C) 2019 World Federation for Ultrasound in Medicine & Biology. All rights reserved.